Multi-fidelity, active learning strategies for exciton transfer among adsorbed molecules

用于光化学应用的新材料至关重要，例如，进一步发展可再生能源设备。这种材料的开发现在通过实验和计算机驱动的分子模拟来解决。理想情况下，包括材料筛选和优化在内的整个设计过程可以在计算机上完成。然而，这需要高效的时间，高精度和易于使用的软件来分析分子聚集体的光化学性质，或者更准确地说，它们的激子性质。该项目的长期目标是开发使这种分析可行的方法，注意到目前通过量子力学/分子力学（QM/MM）方法进行的分子模拟过于昂贵。克服计算挑战的一个有前途的工具是使用廉价的机器学习模型来评估，取代模拟管道中昂贵的量子化学计算。然而，只有这样的机器学习模型确实以适度的成本实现了量子化学训练数据生成的高精度预测，并且可以以（半）自动的方式构建，才能保证该工具的实际长期成功。在这个项目中，我们开发了一个多保真度，主动学习方法的激子转移分子聚集体。多保真度机器学习通过使用在不同量子化学理论水平、基组大小等获得的训练数据的层次结构，有望大大减少所需的高度准确且因此计算昂贵的训练样本的数量。在自动选择最佳可能的训练计算方面将实现进一步的技术改进（主动学习）和双分子模型的构建，即用于依赖于两个分子的性质的机器学习模型。的整体方法被应用于基于分子聚集体的捕光材料的分析。作为这样的聚集体的一个例子，我们专注于吸附在粘土表面的卟啉分子，实验已经显示出有趣的捕光特性。虽然这个模型应用肯定会从我们的新贡献中获益，但我们的兴趣是进一步分享我们在多保真度分子机器学习和QM/MM模拟方面的专业知识和工具。

New materials for photochemical applications are essential, e.g., for the further development of renewable energy devices. The development of such material is nowadays tackled by experiments and by computer-driven molecular simulations. Ideally, the full design process including material screening and optimizations could be done in-silico. This, however, requires time-efficient, high-accuracy and easy-to-use software for the analysis of photochemical properties of molecular aggregates or more precisely their excitonic properties. The long-term goal of this project is to develop methods that will make such an analysis feasible, noting that current molecular simulations by means of quantum mechanics / molecular mechanics (QM/MM) methods are prohibitively expensive. A promising tool to overcome the computational challenges is the use of cheap to evaluate machine learning models, replacing expensive quantum chemical calculations in the simulation pipeline. However, the practical long-term success of this tool can only be guaranteed, if such machine learning models indeed achieve high accuracy predictions at moderate costs for the generation of the quantum chemical training data and can be constructed in a (semi-)automatic way. In this project, we develop a multi-fidelity, active learning approach for exciton transfer within molecular aggregates. Multi-fidelity machine learning promises to strongly reduce the number of required highly accurate and thereby computationally expensive training samples by using hierarchies of training data obtained at different quantum chemical theory levels, basis set sizes, etc. Further technical improvements will be achieved in the automatic selection of best possible training calculations (active learning) and the constructions of bi-molecular models, i.e. machine learning models for properties that depend on two molecules. The overall approach is applied for the analysis of a light-harvesting material based on a molecular aggregate. As an example for such an aggregate, we focus on porphyrin molecules adsorbed on clay surfaces which experimentally have shown to posses interesting light-harvesting properties. While this model application will certainly gain from our novel contributions, our interest is to further share our expertise and tools on multi-fidelity molecular machine learning and on QM/MM simulations within the priority program and beyond.